EP1738306A1 - Systeme a protection informatique et procede pour realiser une reconnaissance et/ou caracterisation automatique de parametres de risque - Google Patents

Systeme a protection informatique et procede pour realiser une reconnaissance et/ou caracterisation automatique de parametres de risque

Info

Publication number
EP1738306A1
EP1738306A1 EP04766531A EP04766531A EP1738306A1 EP 1738306 A1 EP1738306 A1 EP 1738306A1 EP 04766531 A EP04766531 A EP 04766531A EP 04766531 A EP04766531 A EP 04766531A EP 1738306 A1 EP1738306 A1 EP 1738306A1
Authority
EP
European Patent Office
Prior art keywords
risk
relative
data
criteria
classes
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP04766531A
Other languages
German (de)
English (en)
Inventor
Dieter S. Gaubatz
Edward J. Wright
Tracy A. Choka
James P. Eubank
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Swiss Re AG
Original Assignee
Swiss Reinsurance Co Ltd
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Swiss Reinsurance Co Ltd filed Critical Swiss Reinsurance Co Ltd
Publication of EP1738306A1 publication Critical patent/EP1738306A1/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/08—Insurance
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0635—Risk analysis of enterprise or organisation activities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/03—Credit; Loans; Processing thereof
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
    • G06Q40/06—Asset management; Financial planning or analysis

Definitions

  • the present invention relates generally to automated monitoring and / or management of risks, and in particular to automated methods and systems for characterizing relative risks based on a large number of preferred risk criteria.
  • automated monitoring and / or management of risks and in particular to automated methods and systems for characterizing relative risks based on a large number of preferred risk criteria.
  • automated design, development and / or pricing of financial products but generally refers to corresponding monitoring and control systems of risk products and / or populations.
  • One aspect of risk management typically involves considering one or more criteria associated with one or more events of interest.
  • the ability to predict the frequency or eventual probability of the occurrence of such events of interest has value and utility in many cases.
  • Life insurance (and health insurance) is constantly evolving. A relatively recent trend in the field of life insurance is the increasing appearance of "preferred" products. These are products that take into account whether death rates are lower than expectations for "standard living” (i.e. the mean death rates of a healthy population). Insurance companies offer preferred products to people and / or groups who meet selected criteria that indicate low mortality.
  • the objectives are achieved by the invention in that for automated risk parameter detection in
  • Risk management systems by means of relative risk values of a large number of products and / or populations based on product and / or population data records stored in databases that can be accessed, a lookup table with risk parameters is generated that is generated using a filter module Based on the risk parameters of the lookup table, risk classes are assigned to the product and / or population data records, that an analysis module generates at least one expected value for a probability of occurrence of a definable risk event for each risk class and that it is assigned to the risk event, that the expected value is saved using a standardization module of the respective risk class based on an average entry rate of the event for the product and / or population data files is standardized to a relative entry parameter, and that by means of the analysis module a risk characterization value for the respective risk class is generated based on the comparison of the relative entry parameters, the risk characterization value Probability of occurrence of the risk event is determined.
  • a risk characterization value can be determined using the analysis module and compared with available empirical data records for characterizing the product and / or the population, whereby only typical risk characterizations within a definable threshold value are assigned to the risk class.
  • a plurality of risk parameters can also be assigned to one or more of the risk classes, the process being repeated with modification of the risk parameters and the deviations from the expected values being stored assigned to the risk classes.
  • the correlation factors analysis module it is possible, for example, to determine between the risk parameters based on the population data files divided into risk classes, and to store them in an assigned manner in accordance with the corresponding risk parameters.
  • a relevance flag relating to a specific population and / or product can be assigned to each risk parameter, for example by means of one or more threshold values.
  • the lookup table with risk parameters can be generated at least partially dynamically based on product and / or population data records stored in databases. For example, at least one separate relative entry parameter can be generated for sub-risk groups.
  • the data can be adapted to the empirical data if they lie outside a determinable fluctuation tolerance.
  • the storage units can be locally or decentrally accessible via a network. The adjustment can also be made, for example, for the risk parameters using definable variance steps, whereupon the deviation is determined again using the method.
  • the risk parameters can include, for example, at least the relative mortality risks. Based on at least parts of the relative entry parameters, for example, new risk classes can also be generated dynamically.
  • the sub-risk groups can also include, for example, at least gender and / or entry age and / or smoker / non-smoker and / or policy duration. It is clear from the foregoing that, in its broadest interpretation, certain embodiments of the present invention are directed to computerized methods and systems for characterizing relative risks, such as death risks, for a variety of financial products, such as preferred insurance policies.
  • One or more of these embodiments may include the steps of identifying one or more risk classes associated with the plurality of products; Determining an expected entry rate for each risk class; Dividing expected entry rates by an average rate of standard risks to determine a relative risk ratio for each of these risk classes; and comparing the relative risk ratios to characterize the relative risks associated with the variety of products.
  • the present invention also relates to a system for performing these methods.
  • Embodiment variants of the present invention are described below using examples. The examples of the designs are illustrated by the following attached figures:
  • FIG. 1 shows a block diagram which schematically illustrates a system for an embodiment according to the invention.
  • FIG. 2 shows a flow diagram which explains part of an embodiment of a method and a system for characterizing relative risks.
  • Figure 3 is a continuation of the flow diagram of Figure 2.
  • Figure 4 is a continuation of the diagrams of Figures 2 and 3.
  • Figure 5 is a continuation of the diagram of Figure 4.
  • Figure 1 illustrates an architecture that can be used to implement the invention.
  • a flow diagram explains part of an embodiment of a method / system for characterizing relative risks.
  • the risks considered are death risks and especially those based on a variety of preferred risk criteria.
  • the embodiment shown in this and the other figures can be used, for example, to compare and evaluate preferred classifications of risks used by different insurance companies in connection with their respective products. In such a case, different criteria are often used in one or more of the companies to determine which risks are considered to be preferred.
  • the use of the embodiment shown in the figures allows a comparison of preferred insurance products despite the differences in the preferred criteria used by the individual companies.
  • the illustrated system and method can also be used by an individual company to design and / or price a product and for evaluating individual risk exceptions, as will be described in more detail below in connection with the figures.
  • the first step relates to carrying out a predominance research and compiling the results.
  • "Prevailing” means the rate of occurrence of a criterion (or criteria) in an insured population. For example, if one of the preferred criteria is systolic blood pressure, information about the prevalence of the values of the systolic blood pressure and the values that serve as "intersection points" or limit values for classifying an individual risk as standard or as preferred are collected and entered.
  • Block 12 shows the step of recording prevalence data relating to an insured population. For example, an extensive group of laboratory data from insured applicants can be studied to provide information about the prevalence regarding systolic
  • Block 14 shows the step in which the prevalence of preferred criteria is determined in an insured party.
  • a squad is a risk classification that represents a range of incremental probabilities of the occurrence of an insurable event.
  • the activity illustrated by block 14 is accordingly a determination of the occurrence rate of the criterion under consideration among the members of a particular risk classification.
  • the activity illustrated by block 16 relates to
  • the next step, symbolized by block 18, is to determine the predominance of all combinations of correlated ones
  • Prevalence of each individual combination of criteria in a population is determined.
  • a probability of occurrence can be determined for any combination of criteria.
  • These values can be arranged in the form of a matrix, the dimensions of which are equal to the number of preferred criteria that have been considered.
  • Each location in the matrix is a "cell" that contains a value that is specific to a particular combination of criteria.
  • the step represented by block 20 is given the
  • the value of the faulty cell is replaced by a value that matches the pattern that is shown by neighboring, credible ones
  • the part of the procedure depicted in Figure 3 relates to relative mortality (ie, the death rate in preferred classes divided by mean mortality).
  • the first step, represented by block 30, involves performing and collating data from a mortality research. This body of research includes information that is specific to each of the preferred criteria considered. An overview of this information is represented by block 32 in FIG. 3. In addition, other clinical / epidemiological data that are generally accessible in connection with the present preferred criteria are checked (block 34).
  • a relative mortality rate is calculated for each criterion (block 36).
  • relationships to mortality data are also among the various criterion
  • the method continues as shown in FIG. 4 by studying a group of specific base preferred criteria (block 52).
  • the determination of the criterion in this step is usually specific to a customer or a company. This means that the criterion is used that is used by a particular company or insurance product to identify a specific risk, and the present method is used to calculate a basic relative risk ratio ("RRR") for that combination ,
  • the prevalence and relative mortality data is extracted from the memory of these criteria (block 54). After extracting this data, an RRR is calculated for each risk class by age, gender and duration, as indicated in block 56. A specific formula for calculating RRR is given in detail below. The calculations for each risk class are based on prevalence data, relative mortality, and preferred criteria that define each risk class.
  • the results of the calculation, illustrated by block 56, are stored in storage step 58 as indicated.
  • the system then offers a user the opportunity to evaluate alternative scenarios (decision block 60). Examples of alternative scenarios are illustrated by method blocks 62 to 72. These include changing the intersection of preferred criteria (62), adding new criteria (64), removing criteria (66), adding one or more new preferred risk classes (68), removing one or more existing preferred risk classes (70) and changing of the preferred classification system (72).
  • new criteria-specific prevalence data and relative risk ratios are calculated (block 74).
  • the results of the basic prevailing criteria and relative risk ratios are taken from the data that had previously been saved (58), and the recalculated results for prevailing and RRR with the new criteria are compared with the results obtained using the basic criteria , These steps are indicated by blocks 76 and 78 in FIG. 4.
  • the method determines whether the changes are acceptable (decision block 80). If this is the case, the results are saved with the new criteria (58). If the results are unacceptable, changes can be made and additional scenarios considering these changes can be evaluated.
  • the method proceeds as illustrated in the flow diagram in FIG. 5.
  • the results stored in the storage step 58 can optionally be compared with known results that are available in the relevant industry or on the market. This option is represented in FIG. 5 by decision block 82.
  • the results related to criteria used by a customer company will be compared with those of the industry to assess the competitiveness of the client company's risk classifications. This work step is represented in FIG. 5 by block 84. If the results of the comparison are acceptable, the process proceeds as indicated by the "YES" branch exiting decision block 86.
  • the method enables alternatives to be evaluated as described above. If the results of the comparison are unacceptable (for example, if the compared criteria are not considered to be competitive), the method enables alternatives to be evaluated as described above. If the results of the comparison are acceptable, or if no comparisons are made, the method continues as indicated at block 88.
  • client-specific values for predominance and RRR are taken from memory and used to calculate the mortality used in pricing (block 90).
  • low and especially accurate death rates can be generated, and the death rates are stored as indicated by storage step 94.
  • the stored mortality figures can be used to compare the customer's actual mortality experience with the expected mortality values and to develop a pricing of the product.
  • the RRR results can also be used to evaluate preferred exceptions, as decision block 96 indicates. If so, an RRR value is calculated for a single applicant to determine the impact such an exception would have on risk class mortality (block 98).
  • the mean RRR of the risk class is extracted (block 100) and compared with the RRR of the individual applicant (block 102). As decision block 104 indicates, the exception may be permitted (block 106) if the individual RRR is less than or equal to the mean RRR of the risk class. If the individual RRR is greater than the mean RRR of the risk class, the exception can be rejected (block 108).
  • the calculation of RRR values of an individual applicant can also be carried out using sub-categories of criteria (e.g. medical criteria, criteria based on personal or family data, violent deaths, etc.).
  • This tool allows an insurer to accept relatively good risks that would otherwise be rejected because a particular criterion is not met, or reject relatively poor risks that would otherwise be accepted (for example, by an individual who is just qualified on many criteria) ,
  • the use of this tool is not limited to a specific group or subset of criteria.
  • such analyzes can be carried out in relation to other criteria such as motor vehicle reports, participation in dangerous sports or activities, aviation, work abroad, etc.
  • practically any factor that influences an individual's risk of death, whether positive or negative can be included in this tool if the overall suitability is to be assessed as to whether this person can be included in a preferred risk classification.
  • the method ends as indicated by end block 110. It should be added that a new product development cycle can begin for the same client (decision step 60) as an evaluation of an alternative scenario. If there are no significant changes to the data, there is no reason to repeat the steps and procedures described above.
  • RRR formula The relative risk behavior of a particularly preferred class reflects the mortality rate of this risk class in relation to the overall mean rate of a complete distribution of risks classified as "standard life” in the insurance process.
  • the RRR fluctuate according to gender, age of entry, smoking status, preferred risk classes and duration of insurance.
  • a respective risk class ( R1 ) can be defined using the following "n" criterion:
  • M p q st be the relative mortality rate for individuals who have a value of "p” for risk criterion 1, "q” for risk criterion 2, ..., "s” for risk criterion k and "t” for risk criterion n.
  • R ( ⁇ (M pq ... s ... t * p p pqq ... s ... t)) divided by ⁇ P pq ... s ... t
  • RRR each value of the multidimensional splinter matrix
  • Each value of the multidimensional splinter matrix could be referred to as the RRR of a single person or multiple people who exactly meet the criteria associated with that location in the matrix.
  • the comparisons of the RRR are equivalent to a comparison of a splitter group with another splitter group.
  • “Standard” mortality again (ie the mean mortality of a group healthy people). Accordingly, a person with a value of 70 DBP and 130 SBP would have a mortality rate of 85% of the average mortality rate.
  • Company A provides a preferred product that has a criterion that includes a DBP of less than or equal to 70
  • Company B offers a preferred product that has a criterion that has an SBP of less than or equal to 130 is included.
  • Company A's RRR would represent the combination of three splitters (130/70, 131/70 and 132/70) or 86% (ie (85 + 86 + 87) / 3).
  • Company B's RRR would also represent the combination of three splitters (130/70, 130/71 and 130/72) or 96.7% (ie 85 + 95 + 1 10) 73). Although both companies qualified 33% of the total group for their respective preferred products, Company A can offer a lower premium. In this example, Company B would indeed "lose" the 130/70 person because that person can go to Company A and benefit from a lower premium. This would further increase the mortality rate in Society B.
  • the method and system can be implemented using easily accessible computer technology that includes input and output devices, a processor, and data storage. The operation of the system is controlled by a program code that implements the methodology that is explained in the flow diagrams attached. It is not necessary that the method and system require a single machine or that all components of the system be in the same physical location. Alternatively, the method and system can be used as a device or machine for a special purpose can be realized, which was specifically designed to carry out the present invention.

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  • General Business, Economics & Management (AREA)
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  • Marketing (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Technology Law (AREA)
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  • Educational Administration (AREA)
  • Quality & Reliability (AREA)
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Abstract

La présente invention concerne un système à protection informatique et un procédé pour réaliser une reconnaissance et/ou caractérisation automatique de paramètres de risque. Selon l'invention: une table de consultation (4) contenant des paramètres de risque, est produite sur la base d'enregistrements de données de produits et/ou de populations, stockés en étant accessibles, dans des banques de données (2), et des classes de risque sont produites, en étant associées aux enregistrements de produits et/ou de populations, au moyen d'un module de filtrage (3), sur la base des paramètres de risque de la table de consultation (4); un module d'analyse (1) sert à produire pour chaque classe de risque, au moins une valeur escomptée de la probabilité d'apparition d'un événement de risque définissable, et un module de normalisation (5) sert à normaliser la valeur escomptée de la classe de risque respective, sur la base d'une vitesse d'entrée moyenne de l'événement pour les fichiers de données de produits et/ou de populations, par rapport à un paramètre d'entrée relatif; et le module d'analyse (1) produit une valeur de caractérisation de risque pour la classe de risque respective, sur la base de la comparaison entre les paramètres d'entrée relatifs.
EP04766531A 2004-04-01 2004-08-18 Systeme a protection informatique et procede pour realiser une reconnaissance et/ou caracterisation automatique de parametres de risque Withdrawn EP1738306A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US10/816,120 US7849002B2 (en) 2004-04-01 2004-04-01 System and method for evaluating preferred risk definitions
PCT/EP2004/051830 WO2005106725A1 (fr) 2004-04-01 2004-08-18 Systeme a protection informatique et procede pour realiser une reconnaissance et/ou caracterisation automatique de parametres de risque

Publications (1)

Publication Number Publication Date
EP1738306A1 true EP1738306A1 (fr) 2007-01-03

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EP04766531A Withdrawn EP1738306A1 (fr) 2004-04-01 2004-08-18 Systeme a protection informatique et procede pour realiser une reconnaissance et/ou caracterisation automatique de parametres de risque

Country Status (3)

Country Link
US (2) US7849002B2 (fr)
EP (1) EP1738306A1 (fr)
WO (1) WO2005106725A1 (fr)

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WO2017220140A1 (fr) * 2016-06-22 2017-12-28 Swiss Reinsurance Company Ltd. Système d'assurance-vie à processus de souscription entièrement automatisé pour une souscription et un réglage du risque en temps réel, et son procédé correspondant
CN109255486B (zh) * 2018-09-14 2021-07-20 创新先进技术有限公司 一种策略配置的优化方法及装置
CN111260168B (zh) * 2018-11-30 2023-09-29 内蒙古伊利实业集团股份有限公司 食品安全风险识别方法与系统

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Also Published As

Publication number Publication date
US20050240514A1 (en) 2005-10-27
US7849002B2 (en) 2010-12-07
US20080281645A1 (en) 2008-11-13
WO2005106725A1 (fr) 2005-11-10

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